Impact of land use and food environment on risk of type 2 diabetes: A national study of veterans, 2008-2018

GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies

The industrial revolution and urbanization fundamentally restructured populations' living circumstances, often with poor impacts on health. As an example, unhealthy food establishments may concentrate in some neighborhoods and, mediated by social and commercial drivers, increase local health risks. To understand the connections between neighborhood food environments and public health, researchers often use geographic information systems (GIS) and spatial statistics to analyze place-based evidence, but such tools require careful application and interpretation. In this article, we summarize the factors shaping neighborhood health in relation to local food environments and outline the use of GIS methodologies to assess associations between the two. We provide an overview of available data sources, analytical approaches, and their strengths and weaknesses. We postulate next steps in GIS integration with forecasting, prediction, and simulation measures to frame implications for local health policies.

Demographic, social and geographic factors associated with glycaemic control among US Veterans with new onset type 2 diabetes: a retrospective cohort study

OBJECTIVES

This study evaluated whether a range of demographic, social and geographic factors had an influence on glycaemic control longitudinally after an initial diagnosis of diabetes.

DESIGN, SETTING AND PARTICIPANTS

We used the US Veterans Administration Diabetes Risk national cohort to track glycaemic control among patients 20-79-year old with a new diagnosis of type 2 diabetes.

PRIMARY OUTCOME AND METHODS

We modelled associations between glycaemic control at follow-up clinical assessments and geographic factors including neighbourhood race/ethnicity, socioeconomic, land use and food environment measures. We also adjusted for individual demographics, comorbidities, haemoglobin A1c (HbA1c) at diagnosis and duration of follow-up. These factors were analysed within strata of community type: high-density urban, low-density urban, suburban/small town and rural areas.

RESULTS

We analysed 246 079 Veterans who developed a new type 2 diabetes diagnosis in 2008-2018 and had at least 2 years of follow-up data available. Across all community types, we found that lower baseline HbA1c and female sex were strongly associated with a higher likelihood of within-range HbA1c at follow-up. Surprisingly, patients who were older or had more documented comorbidities were more likely to have within-range follow-up HbA1c results. While there was variation by community type, none of the geographic measures analysed consistently demonstrated significant associations with glycaemic control across all community types.

Concepts, Characterizations, and Cautions: A Public Health Guide and Glossary for Planning Food Environment Measurement

Background

There is no singular approach to measuring the food environment suitable for all studies. Understanding terminology, methodology, and common issues is crucial to choosing the best approach.

Objective

This review is designed to support a shared understanding so diverse multi-institutional teams engaged in food environment measurement can justify their measurement choices and have informed discussions about reasons for measurement strategies to vary across projects.

Methods

This guide defines key terms and provides annotated resources identified as a useful starting point for exploring the food environment literature. The writing team was an academic-practice collaboration, reflecting on the experience of a multi-institutional team focused on retail environments across the US relevant to cardiovascular disease.

Results

Terms and annotated resources are divided into three sections: food environment constructs, classification and measures, and errors and strategies to reduce error. Two examples of methods and challenges encountered while measuring the food environment in the context of a US health department are provided. Researchers and practice professionals are directed to the Food Environment Electronic Database Directory (https://www.foodenvironmentdirectory.com/) for comparing available data resources for food environment measurement, focused on the US; this resource incorporates updates informed by user input and literature reviews.

Discussion

Measuring the food environment is complex and risks oversimplification. This guide serves as a starting point but only partially captures some aspects of neighborhood food environment measurement.

Conclusions

No single food environment measure or data source meets all research and practice objectives. This shared starting point can facilitate theoretically grounded food environment measurement.

Assessing the association between food environment and dietary inflammation by community type: a cross-sectional REGARDS study

BACKGROUND

Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors.

OBJECTIVE

This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US.

METHODS

Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together.

RESULTS

Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas.

CONCLUSIONS

The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.